"""simple docstring"""


from __future__ import annotations

import os
import tempfile
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow

from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import (
	    AutoTokenizer,
	    TFAutoModelForCausalLM,
	    TFAutoModelForSeqaSeqLM,
	    TFAutoModelForSpeechSeqaSeq,
	    TFAutoModelForVisionaSeq,
	    TFBartForConditionalGeneration,
	    TFLogitsProcessorList,
	    TFMinLengthLogitsProcessor,
	    tf_top_k_top_p_filtering,
	)

if is_tensorflow_text_available():
	import tensorflow_text as text






@require_tf
class   __UpperCAmelCase		(      unittest.TestCase      ):

				def 						_a      ( self ):
										lowerCamelCase__   =tf.convert_to_tensor(
										    [
										        [
										            8.2_2_2_0_9_9_1,  # 3rd highest value; idx. 0
										            -0.5_6_2_0_0_4_4,
										            5.2_3_2_2_9_7_5_2,
										            4.0_3_8_6_3_9_3,
										            -6.8_7_9_8_3_7_8,
										            -0.5_4_7_8_5_8_0_2,
										            -3.2_0_1_2_1_5_3,
										            2.9_2_7_7_7_1_7_6,
										            1.8_8_1_7_1_9_5_3,
										            7.3_5_3_4_1_2_7_6,  # 5th highest value; idx. 9
										            8.4_3_2_0_7_8_3_3,  # 2nd highest value; idx. 10
										            -9.8_5_7_1_1_8_3_6,
										            -5.9_6_2_0_9_2_3_6,
										            -1.1_3_0_3_9_1_6_1,
										            -7.1_1_1_5_2_9_4,
										            -0.8_3_6_9_6_3_3,
										            -5.3_1_8_6_4_0_8,
										            7.0_6_4_2_7_4_0_7,
										            0.8_1_3_6_9_3_4_4,
										            -0.8_2_0_2_3_8_1_7,
										            -5.9_1_7_9_7_9_6,
										            0.5_8_8_1_3_4_4_3,
										            -6.9_9_7_7_8_4_3_8,
										            4.7_1_5_5_1_1_8_9,
										            -0.1_8_7_7_1_6_3_7,
										            7.4_4_0_2_0_7_5_9,  # 4th highest value; idx. 25
										            9.3_8_4_5_0_9_8_7,  # 1st highest value; idx. 26
										            2.1_2_6_6_2_9_4_1,
										            -9.3_2_5_6_2_0_3_8,
										            2.3_5_6_5_2_5_2_2,
										        ],  # cummulative prob of 5 highest values <= 0.6
										        [
										            0.5_8_4_2_5_5_1_8,
										            4.5_3_1_3_9_2_3_8,
										            -5.5_7_5_1_0_4_6_4,
										            -6.2_8_0_3_0_6_9_9,
										            -7.1_9_5_2_9_5_0_3,
										            -4.0_2_1_2_2_5_5_1,
										            1.3_9_3_3_7_0_3_7,
										            -6.0_6_7_0_7_0_5_7,
										            1.5_9_4_8_0_5_1_7,
										            -9.6_4_3_1_1_9,
										            0.0_3_9_0_7_7_9_9,
										            0.6_7_2_3_1_7_6_2,
										            -8.8_8_2_0_6_7_2_6,
										            6.2_7_1_1_5_9_2_2,  # 4th highest value; idx. 13
										            2.2_8_5_2_0_7_2_3,
										            4.8_2_7_6_7_5_0_6,
										            4.3_0_4_2_1_3_6_8,
										            8.8_2_7_5_3_1_3,  # 2nd highest value; idx. 17
										            5.4_4_0_2_9_9_5_8,  # 5th highest value; idx. 18
										            -4.4_7_3_5_7_9_4,
										            7.3_8_5_7_9_5_3_6,  # 3rd highest value; idx. 20
										            -2.9_1_0_5_1_6_6_3,
										            2.6_1_9_4_6_0_7_7,
										            -2.5_6_7_4_7_6_2,
										            -9.4_8_9_5_9_3_0_2,
										            -4.0_2_9_2_2_6_4_5,
										            -1.3_5_4_1_6_9_1_8,
										            9.6_7_7_0_2_3_2_3,  # 1st highest value; idx. 27
										            -5.8_9_4_7_8_5_5_3,
										            1.8_5_3_7_0_4_6_7,
										        ],  # cummulative prob of 5 highest values <= 0.6
										    ]      ,					dtype=tf.floataa      ,					)

										lowerCamelCase__   =tf.convert_to_tensor(
										    [[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]]      ,					dtype=tf.intaa      ,					)  # expected non filtered idx as noted above

										lowerCamelCase__   =tf.convert_to_tensor(
										    [8.2_2_2_0_9_9, 7.3_5_3_4_1_2_6, 8.4_3_2_0_7_8, 7.4_4_0_2_0_7_5, 9.3_8_4_5_1, 6.2_7_1_1_5_9, 8.8_2_7_5_3_1, 5.4_4_0_2_9_9_5, 7.3_8_5_7_9_5_6, 9.6_7_7_0_2_3]      ,					dtype=tf.floataa      ,					)  # expected non filtered values as noted above

										lowerCamelCase__   =tf_top_k_top_p_filtering(_lowerCamelCase      ,					top_k=10      ,					top_p=0.6      ,					min_tokens_to_keep=4 )

										lowerCamelCase__   =output[output != -float("inf" )]
										lowerCamelCase__   =tf.cast(
										    tf.where(tf.not_equal(_lowerCamelCase      ,					tf.constant(-float("inf" )      ,					dtype=tf.floataa ) ) )      ,					dtype=tf.intaa      ,					)

										tf.debugging.assert_near(_lowerCamelCase      ,					_lowerCamelCase      ,					rtol=1E-12 )
										tf.debugging.assert_equal(_lowerCamelCase      ,					_lowerCamelCase )






@require_tf
class   __UpperCAmelCase		(      unittest.TestCase , __lowerCAmelCase      ):
				# setting framework_dependent_parameters needs to be gated, just like its contents' imports
				if is_tf_available():
								A__ :  List[str]    				=			{
								    '''AutoModelForCausalLM''': TFAutoModelForCausalLM,
								    '''AutoModelForSpeechSeq2Seq''': TFAutoModelForSpeechSeqaSeq,
								    '''AutoModelForSeq2SeqLM''': TFAutoModelForSeqaSeqLM,
								    '''AutoModelForVision2Seq''': TFAutoModelForVisionaSeq,
								    '''LogitsProcessorList''': TFLogitsProcessorList,
								    '''MinLengthLogitsProcessor''': TFMinLengthLogitsProcessor,
								    '''create_tensor_fn''': tf.convert_to_tensor,
								    '''floats_tensor''': floats_tensor,
								    '''return_tensors''': '''tf''',
								}

				@slow
				def 						_a      ( self ):
										# TF-only test: tf.saved_model export
										lowerCamelCase__   =TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
										lowerCamelCase__   =2
										lowerCamelCase__   =2






										class   __UpperCAmelCase		(      tf.Module      ):

														def __init__( self      ,					_lowerCamelCase ):
																				super(_lowerCamelCase      ,					self ).__init__()
																				lowerCamelCase__   =model






														@tf.function(
														    input_signature=(
														        tf.TensorSpec((None, input_length)      ,					tf.intaa      ,					name="input_ids" ),
														        tf.TensorSpec((None, input_length)      ,					tf.intaa      ,					name="attention_mask" ),
														    )      ,					jit_compile=_lowerCamelCase      ,					)
														def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):
																				lowerCamelCase__   =self.model.generate(
																				    input_ids=_lowerCamelCase      ,					attention_mask=_lowerCamelCase      ,					max_new_tokens=_lowerCamelCase      ,					return_dict_in_generate=_lowerCamelCase      ,					)
																				return {"sequences": outputs["sequences"]}





										lowerCamelCase__   =[[2, 0], [102, 103]]
										lowerCamelCase__   =[[1, 0], [1, 1]]
										lowerCamelCase__   =DummyModel(model=_lowerCamelCase )
										with tempfile.TemporaryDirectory() as tmp_dir:
																tf.saved_model.save(_lowerCamelCase      ,					_lowerCamelCase      ,					signatures={"serving_default": dummy_model.serving} )
																lowerCamelCase__   =tf.saved_model.load(_lowerCamelCase ).signatures["serving_default"]
																for batch_size in range(1      ,					len(_lowerCamelCase ) + 1 ):
																						lowerCamelCase__   ={
																						    "input_ids": tf.constant(dummy_input_ids[:batch_size] ),
																						    "attention_mask": tf.constant(dummy_attention_masks[:batch_size] ),
																						}
																						lowerCamelCase__   =serving_func(**_lowerCamelCase )["sequences"]
																						lowerCamelCase__   =test_model.generate(**_lowerCamelCase      ,					max_new_tokens=_lowerCamelCase )
																						tf.debugging.assert_equal(_lowerCamelCase      ,					_lowerCamelCase )

				@slow
				def 						_a      ( self ):
										# TF-only test: tf.saved_model export
										lowerCamelCase__   =TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
										lowerCamelCase__   =1
										lowerCamelCase__   =2






										class   __UpperCAmelCase		(      tf.Module      ):

														def __init__( self      ,					_lowerCamelCase ):
																				super(_lowerCamelCase      ,					self ).__init__()
																				lowerCamelCase__   =model






														@tf.function(
														    input_signature=(
														        tf.TensorSpec((batch_size, None)      ,					tf.intaa      ,					name="input_ids" ),
														        tf.TensorSpec((batch_size, None)      ,					tf.intaa      ,					name="attention_mask" ),
														    )      ,					jit_compile=_lowerCamelCase      ,					)
														def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):
																				lowerCamelCase__   =self.model.generate(
																				    input_ids=_lowerCamelCase      ,					attention_mask=_lowerCamelCase      ,					max_new_tokens=_lowerCamelCase      ,					return_dict_in_generate=_lowerCamelCase      ,					)
																				return {"sequences": outputs["sequences"]}





										lowerCamelCase__   =[[2], [102, 103]]
										lowerCamelCase__   =[[1], [1, 1]]
										lowerCamelCase__   =DummyModel(model=_lowerCamelCase )
										with tempfile.TemporaryDirectory() as tmp_dir:
																tf.saved_model.save(_lowerCamelCase      ,					_lowerCamelCase      ,					signatures={"serving_default": dummy_model.serving} )
																lowerCamelCase__   =tf.saved_model.load(_lowerCamelCase ).signatures["serving_default"]
																for input_row in range(len(_lowerCamelCase ) ):
																						lowerCamelCase__   ={
																						    "input_ids": tf.constant([dummy_input_ids[input_row]] ),
																						    "attention_mask": tf.constant([dummy_attention_masks[input_row]] ),
																						}
																						lowerCamelCase__   =serving_func(**_lowerCamelCase )["sequences"]
																						lowerCamelCase__   =test_model.generate(**_lowerCamelCase      ,					max_new_tokens=_lowerCamelCase )
																						tf.debugging.assert_equal(_lowerCamelCase      ,					_lowerCamelCase )

				@slow
				@require_tensorflow_text
				def 						_a      ( self ):
										# TF-only test: tf.saved_model export
										with tempfile.TemporaryDirectory() as tmp_dir:
																# file needed to load the TF tokenizer
																hf_hub_download(repo_id="google/flan-t5-small"      ,					filename="spiece.model"      ,					local_dir=_lowerCamelCase )






																class   __UpperCAmelCase		(      tf.keras.layers.Layer      ):

																				def __init__( self ):
																										super().__init__()
																										lowerCamelCase__   =text.SentencepieceTokenizer(
																										    model=tf.io.gfile.GFile(os.path.join(_lowerCamelCase      ,					"spiece.model" )      ,					"rb" ).read() )
																										lowerCamelCase__   =TFAutoModelForSeqaSeqLM.from_pretrained("hf-internal-testing/tiny-random-t5" )






																				def 						_a      ( self      ,					_lowerCamelCase      ,					*_lowerCamelCase      ,					**_lowerCamelCase ):
																										lowerCamelCase__   =self.tokenizer.tokenize(_lowerCamelCase )
																										lowerCamelCase__   , lowerCamelCase__   =text.pad_model_inputs(
																										    _lowerCamelCase      ,					max_seq_length=64      ,					pad_value=self.model.config.pad_token_id )
																										lowerCamelCase__   =self.model.generate(input_ids=_lowerCamelCase      ,					attention_mask=_lowerCamelCase )
																										return self.tokenizer.detokenize(_lowerCamelCase )





																lowerCamelCase__   =CompleteSentenceTransformer()
																lowerCamelCase__   =tf.keras.layers.Input(shape=(1,)      ,					dtype=tf.string      ,					name="inputs" )
																lowerCamelCase__   =complete_model(_lowerCamelCase )
																lowerCamelCase__   =tf.keras.Model(_lowerCamelCase      ,					_lowerCamelCase )
																keras_model.save(_lowerCamelCase )

				def 						_a      ( self ):
										# Has PT equivalent: this test relies on random sampling
										lowerCamelCase__   ={
										    "do_sample": True,
										    "num_beams": 1,
										    "top_p": 0.7,
										    "top_k": 10,
										    "temperature": 0.7,
										}
										lowerCamelCase__   =14

										lowerCamelCase__   =AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
										lowerCamelCase__   ="Hello, my dog is cute and"
										lowerCamelCase__   =tokenizer(_lowerCamelCase      ,					return_tensors="tf" )
										lowerCamelCase__   =TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2" )

										lowerCamelCase__   =638
										# forces the generation to happen on CPU, to avoid GPU-related quirks
										with tf.device(":/CPU:0" ):
																tf.random.set_seed(0 )
																lowerCamelCase__   =model.generate(**_lowerCamelCase      ,					eos_token_id=_lowerCamelCase      ,					**_lowerCamelCase )
										self.assertTrue(expectation == len(generated_tokens[0] ) )

										lowerCamelCase__   =[638, 198]
										with tf.device(":/CPU:0" ):
																tf.random.set_seed(0 )
																lowerCamelCase__   =model.generate(**_lowerCamelCase      ,					eos_token_id=_lowerCamelCase      ,					**_lowerCamelCase )
										self.assertTrue(expectation == len(generated_tokens[0] ) )






				def 						_a      ( self ):
										# Has PT equivalent: ample use of framework-specific code
										lowerCamelCase__   =AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart" )
										lowerCamelCase__   ="Hugging Face is a technology company based in New York and Paris."
										lowerCamelCase__   =bart_tokenizer(_lowerCamelCase      ,					return_tensors="tf" ).input_ids
										lowerCamelCase__   =TFBartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart" )
										lowerCamelCase__   =bart_model.generate(_lowerCamelCase ).numpy()






										class   __UpperCAmelCase		(      __lowerCAmelCase      ):

														def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase=None      ,					**_lowerCamelCase ):
																				return super().call(_lowerCamelCase      ,					**_lowerCamelCase )





										lowerCamelCase__   =FakeBart.from_pretrained("hf-internal-testing/tiny-random-bart" )
										lowerCamelCase__   =bart_model.generate(_lowerCamelCase      ,					foo="bar" ).numpy()
										self.assertTrue(np.array_equal(_lowerCamelCase      ,					_lowerCamelCase ) )






										class   __UpperCAmelCase		(      bart_model.model.encoder.__class__      ):

														def 						_a      ( self      ,					_lowerCamelCase      ,					**_lowerCamelCase ):
																				return super().call(_lowerCamelCase      ,					**_lowerCamelCase )





										lowerCamelCase__   =FakeEncoder(bart_model.config      ,					bart_model.model.shared )
										lowerCamelCase__   =fake_encoder

										# Normal generation still works (the output will be different because the encoder weights are different)
										lowerCamelCase__   =bart_model.generate(_lowerCamelCase ).numpy()
										with self.assertRaises(_lowerCamelCase ):
																# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
																bart_model.generate(_lowerCamelCase      ,					foo="bar" )



"""simple docstring"""


import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()






class   __UpperCAmelCase		(      unittest.TestCase      ):
				A__ :  List[Any]    				=			StableDiffusionLDMaDPipeline
				A__ :  List[str]    				=			TEXT_TO_IMAGE_PARAMS
				A__ :  str    				=			TEXT_TO_IMAGE_BATCH_PARAMS
				A__ :  Tuple    				=			TEXT_TO_IMAGE_IMAGE_PARAMS

				def 						_a      ( self ):
										torch.manual_seed(0 )
										lowerCamelCase__   =UNetaDConditionModel(
										    block_out_channels=(32, 64)      ,					layers_per_block=2      ,					sample_size=32      ,					in_channels=4      ,					out_channels=4      ,					down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,					up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,					cross_attention_dim=32      ,					)
										lowerCamelCase__   =DDIMScheduler(
										    beta_start=0.0_0_0_8_5      ,					beta_end=0.0_1_2      ,					beta_schedule="scaled_linear"      ,					clip_sample=_lowerCamelCase      ,					set_alpha_to_one=_lowerCamelCase      ,					)
										torch.manual_seed(0 )
										lowerCamelCase__   =AutoencoderKL(
										    block_out_channels=[32, 64]      ,					in_channels=6      ,					out_channels=6      ,					down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]      ,					up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]      ,					latent_channels=4      ,					)
										torch.manual_seed(0 )
										lowerCamelCase__   =CLIPTextConfig(
										    bos_token_id=0      ,					eos_token_id=2      ,					hidden_size=32      ,					intermediate_size=37      ,					layer_norm_eps=1E-05      ,					num_attention_heads=4      ,					num_hidden_layers=5      ,					pad_token_id=1      ,					vocab_size=1000      ,					)
										lowerCamelCase__   =CLIPTextModel(_lowerCamelCase )
										lowerCamelCase__   =CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

										lowerCamelCase__   ={
										    "unet": unet,
										    "scheduler": scheduler,
										    "vae": vae,
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "safety_checker": None,
										    "feature_extractor": None,
										}
										return components

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase=0 ):
										if str(_lowerCamelCase ).startswith("mps" ):
																lowerCamelCase__   =torch.manual_seed(_lowerCamelCase )
										else:
																lowerCamelCase__   =torch.Generator(device=_lowerCamelCase ).manual_seed(_lowerCamelCase )
										lowerCamelCase__   ={
										    "prompt": "A painting of a squirrel eating a burger",
										    "generator": generator,
										    "num_inference_steps": 2,
										    "guidance_scale": 6.0,
										    "output_type": "numpy",
										}
										return inputs

				def 						_a      ( self ):
										lowerCamelCase__   ="cpu"  # ensure determinism for the device-dependent torch.Generator

										lowerCamelCase__   =self.get_dummy_components()
										lowerCamelCase__   =StableDiffusionLDMaDPipeline(**_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe.to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth

										lowerCamelCase__   =rgb[0, -3:, -3:, -1]
										lowerCamelCase__   =depth[0, -3:, -1]

										assert rgb.shape == (1, 64, 64, 3)
										assert depth.shape == (1, 64, 64)

										lowerCamelCase__   =np.array(
										    [0.3_7_3_3_8_1_7_6, 0.7_0_2_4_7, 0.7_4_2_0_3_1_9_3, 0.5_1_6_4_3_6_0_4, 0.5_8_2_5_6_7_9_3, 0.6_0_9_3_2_1_3_6, 0.4_1_8_1_0_9_5, 0.4_8_3_5_5_8_7_7, 0.4_6_5_3_5_2_6_2] )
										lowerCamelCase__   =np.array([1_0_3.4_6_7_2_7, 8_5.8_1_2_0_0_4, 8_7.8_4_9_2_3_6] )

										assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb ).max() < 1E-2
										assert np.abs(image_slice_depth.flatten() - expected_slice_depth ).max() < 1E-2

				def 						_a      ( self ):
										lowerCamelCase__   =self.get_dummy_components()
										lowerCamelCase__   =StableDiffusionLDMaDPipeline(**_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe.to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase )
										lowerCamelCase__   =3 * [inputs["prompt"]]

										# forward
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth
										lowerCamelCase__   =rgb_slice_a[0, -3:, -3:, -1]
										lowerCamelCase__   =depth_slice_a[0, -3:, -1]

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase )
										lowerCamelCase__   =3 * [inputs.pop("prompt" )]

										lowerCamelCase__   =ldmad_pipe.tokenizer(
										    _lowerCamelCase      ,					padding="max_length"      ,					max_length=ldmad_pipe.tokenizer.model_max_length      ,					truncation=_lowerCamelCase      ,					return_tensors="pt"      ,					)
										lowerCamelCase__   =text_inputs["input_ids"].to(_lowerCamelCase )

										lowerCamelCase__   =ldmad_pipe.text_encoder(_lowerCamelCase )[0]

										lowerCamelCase__   =prompt_embeds

										# forward
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth
										lowerCamelCase__   =rgb_slice_a[0, -3:, -3:, -1]
										lowerCamelCase__   =depth_slice_a[0, -3:, -1]

										assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten() ).max() < 1E-4
										assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten() ).max() < 1E-4






				def 						_a      ( self ):
										lowerCamelCase__   ="cpu"  # ensure determinism for the device-dependent torch.Generator
										lowerCamelCase__   =self.get_dummy_components()
										lowerCamelCase__   =PNDMScheduler(skip_prk_steps=_lowerCamelCase )
										lowerCamelCase__   =StableDiffusionLDMaDPipeline(**_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe.to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase )
										lowerCamelCase__   ="french fries"
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase      ,					negative_prompt=_lowerCamelCase )

										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth
										lowerCamelCase__   =rgb[0, -3:, -3:, -1]
										lowerCamelCase__   =depth[0, -3:, -1]

										assert rgb.shape == (1, 64, 64, 3)
										assert depth.shape == (1, 64, 64)

										lowerCamelCase__   =np.array(
										    [0.3_7_0_4_4, 0.7_1_8_1_1_5_0_3, 0.7_2_2_3_2_5_1, 0.4_8_6_0_3_6_7_5, 0.5_6_3_8_3_9_1, 0.6_3_6_4_9_4_8, 0.4_2_8_3_3_7_0_4, 0.4_9_0_1_3_1_5, 0.4_7_9_2_6_2_1_7] )
										lowerCamelCase__   =np.array([1_0_7.8_4_7_3_8, 8_4.6_2_8_0_2, 8_9.9_6_2_1_3_5] )
										assert np.abs(rgb_slice.flatten() - expected_slice_rgb ).max() < 1E-2
										assert np.abs(depth_slice.flatten() - expected_slice_depth ).max() < 1E-2






@slow
@require_torch_gpu
class   __UpperCAmelCase		(      unittest.TestCase      ):

				def 						_a      ( self ):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase="cpu"      ,					_lowerCamelCase=torch.floataa      ,					_lowerCamelCase=0 ):
										lowerCamelCase__   =torch.Generator(device=_lowerCamelCase ).manual_seed(_lowerCamelCase )
										lowerCamelCase__   =np.random.RandomState(_lowerCamelCase ).standard_normal((1, 4, 64, 64) )
										lowerCamelCase__   =torch.from_numpy(_lowerCamelCase ).to(device=_lowerCamelCase      ,					dtype=_lowerCamelCase )
										lowerCamelCase__   ={
										    "prompt": "a photograph of an astronaut riding a horse",
										    "latents": latents,
										    "generator": generator,
										    "num_inference_steps": 3,
										    "guidance_scale": 7.5,
										    "output_type": "numpy",
										}
										return inputs






				def 						_a      ( self ):
										lowerCamelCase__   =StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" )
										lowerCamelCase__   =ldmad_pipe.to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_inputs(_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth
										lowerCamelCase__   =rgb[0, -3:, -3:, -1].flatten()
										lowerCamelCase__   =rgb[0, -3:, -1].flatten()

										assert rgb.shape == (1, 512, 512, 3)
										assert depth.shape == (1, 512, 512)

										lowerCamelCase__   =np.array(
										    [0.5_3_8_0_5_4_6_5, 0.5_6_7_0_7_3_0_5, 0.5_4_8_6_5_1_5, 0.5_7_0_1_2_2_3_6, 0.5_8_1_4_5_1_1, 0.5_6_2_5_3_4_8_7, 0.5_4_8_4_3_0_1_4, 0.5_5_0_9_2_2_6_3, 0.6_4_5_9_7_0_6] )
										lowerCamelCase__   =np.array(
										    [0.9_2_6_3_7_8_1, 0.6_6_7_8_6_7_2, 0.5_4_8_6_5_1_5, 0.9_2_2_0_2_1_4_5, 0.6_7_8_3_1_1_3_5, 0.5_6_2_5_3_4_8_7, 0.9_2_4_1_6_9_4, 0.7_5_5_1_4_7_8, 0.6_4_5_9_7_0_6] )
										assert np.abs(rgb_slice - expected_slice_rgb ).max() < 3E-3
										assert np.abs(depth_slice - expected_slice_depth ).max() < 3E-3






@nightly
@require_torch_gpu
class   __UpperCAmelCase		(      unittest.TestCase      ):

				def 						_a      ( self ):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase="cpu"      ,					_lowerCamelCase=torch.floataa      ,					_lowerCamelCase=0 ):
										lowerCamelCase__   =torch.Generator(device=_lowerCamelCase ).manual_seed(_lowerCamelCase )
										lowerCamelCase__   =np.random.RandomState(_lowerCamelCase ).standard_normal((1, 4, 64, 64) )
										lowerCamelCase__   =torch.from_numpy(_lowerCamelCase ).to(device=_lowerCamelCase      ,					dtype=_lowerCamelCase )
										lowerCamelCase__   ={
										    "prompt": "a photograph of an astronaut riding a horse",
										    "latents": latents,
										    "generator": generator,
										    "num_inference_steps": 50,
										    "guidance_scale": 7.5,
										    "output_type": "numpy",
										}
										return inputs

				def 						_a      ( self ):
										lowerCamelCase__   =StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" ).to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_inputs(_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth

										lowerCamelCase__   =0.4_9_5_5_8_6
										lowerCamelCase__   =0.3_3_7_9_5_5_1_5
										lowerCamelCase__   =1_1_2.4_8_5_1_8
										lowerCamelCase__   =9_8.4_8_9_7_4_6
										assert np.abs(expected_rgb_mean - rgb.mean() ) < 1E-3
										assert np.abs(expected_rgb_std - rgb.std() ) < 1E-3
										assert np.abs(expected_depth_mean - depth.mean() ) < 1E-3
										assert np.abs(expected_depth_std - depth.std() ) < 1E-3






				def 						_a      ( self ):
										lowerCamelCase__   =StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d-4c" ).to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_inputs(_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth

										lowerCamelCase__   =0.4_1_9_4_1_2_7
										lowerCamelCase__   =0.3_5_3_7_5_5_8_6
										lowerCamelCase__   =0.5_6_3_8_5_0_2
										lowerCamelCase__   =0.3_4_6_8_6_1_0_3

										assert rgb.shape == (1, 512, 512, 3)
										assert depth.shape == (1, 512, 512, 1)
										assert np.abs(expected_rgb_mean - rgb.mean() ) < 1E-3
										assert np.abs(expected_rgb_std - rgb.std() ) < 1E-3
										assert np.abs(expected_depth_mean - depth.mean() ) < 1E-3
										assert np.abs(expected_depth_std - depth.std() ) < 1E-3


